from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-23 14:10:33.195907
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 23, Dec, 2020
Time: 14:10:37
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.9794
Nobs: 149.000 HQIC: -45.0567
Log likelihood: 1598.85 FPE: 1.29644e-20
AIC: -45.7939 Det(Omega_mle): 7.22529e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.467204 0.164342 2.843 0.004
L1.Burgenland 0.142880 0.082718 1.727 0.084
L1.Kärnten -0.237131 0.066499 -3.566 0.000
L1.Niederösterreich 0.099903 0.194214 0.514 0.607
L1.Oberösterreich 0.253734 0.164553 1.542 0.123
L1.Salzburg 0.177293 0.085221 2.080 0.037
L1.Steiermark 0.082204 0.119335 0.689 0.491
L1.Tirol 0.149342 0.078669 1.898 0.058
L1.Vorarlberg 0.006373 0.076833 0.083 0.934
L1.Wien -0.123884 0.159975 -0.774 0.439
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.525696 0.214857 2.447 0.014
L1.Burgenland 0.009957 0.108144 0.092 0.927
L1.Kärnten 0.361051 0.086939 4.153 0.000
L1.Niederösterreich 0.106212 0.253911 0.418 0.676
L1.Oberösterreich -0.185049 0.215132 -0.860 0.390
L1.Salzburg 0.195701 0.111416 1.756 0.079
L1.Steiermark 0.248163 0.156016 1.591 0.112
L1.Tirol 0.143665 0.102851 1.397 0.162
L1.Vorarlberg 0.187491 0.100450 1.867 0.062
L1.Wien -0.574973 0.209148 -2.749 0.006
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.293038 0.071667 4.089 0.000
L1.Burgenland 0.106358 0.036072 2.948 0.003
L1.Kärnten -0.026182 0.028999 -0.903 0.367
L1.Niederösterreich 0.065735 0.084694 0.776 0.438
L1.Oberösterreich 0.289399 0.071759 4.033 0.000
L1.Salzburg -0.002216 0.037164 -0.060 0.952
L1.Steiermark -0.019604 0.052040 -0.377 0.706
L1.Tirol 0.089131 0.034307 2.598 0.009
L1.Vorarlberg 0.132102 0.033506 3.943 0.000
L1.Wien 0.079157 0.069763 1.135 0.257
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.184922 0.082550 2.240 0.025
L1.Burgenland -0.009178 0.041550 -0.221 0.825
L1.Kärnten 0.021613 0.033403 0.647 0.518
L1.Niederösterreich 0.014557 0.097555 0.149 0.881
L1.Oberösterreich 0.414219 0.082656 5.011 0.000
L1.Salzburg 0.098194 0.042807 2.294 0.022
L1.Steiermark 0.193562 0.059943 3.229 0.001
L1.Tirol 0.031021 0.039516 0.785 0.432
L1.Vorarlberg 0.102154 0.038594 2.647 0.008
L1.Wien -0.052884 0.080357 -0.658 0.510
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.565689 0.173545 3.260 0.001
L1.Burgenland 0.074167 0.087350 0.849 0.396
L1.Kärnten 0.007393 0.070222 0.105 0.916
L1.Niederösterreich -0.060283 0.205090 -0.294 0.769
L1.Oberösterreich 0.156733 0.173768 0.902 0.367
L1.Salzburg 0.049823 0.089994 0.554 0.580
L1.Steiermark 0.129707 0.126018 1.029 0.303
L1.Tirol 0.213842 0.083075 2.574 0.010
L1.Vorarlberg 0.016774 0.081136 0.207 0.836
L1.Wien -0.138448 0.168934 -0.820 0.412
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.166534 0.119868 1.389 0.165
L1.Burgenland -0.033785 0.060333 -0.560 0.575
L1.Kärnten -0.014399 0.048503 -0.297 0.767
L1.Niederösterreich 0.165780 0.141656 1.170 0.242
L1.Oberösterreich 0.410206 0.120022 3.418 0.001
L1.Salzburg -0.023842 0.062159 -0.384 0.701
L1.Steiermark -0.046095 0.087041 -0.530 0.596
L1.Tirol 0.189325 0.057380 3.299 0.001
L1.Vorarlberg 0.035974 0.056041 0.642 0.521
L1.Wien 0.159708 0.116683 1.369 0.171
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.208466 0.150986 1.381 0.167
L1.Burgenland 0.076362 0.075996 1.005 0.315
L1.Kärnten -0.042013 0.061094 -0.688 0.492
L1.Niederösterreich -0.027267 0.178430 -0.153 0.879
L1.Oberösterreich -0.122640 0.151179 -0.811 0.417
L1.Salzburg 0.013009 0.078295 0.166 0.868
L1.Steiermark 0.384610 0.109637 3.508 0.000
L1.Tirol 0.517662 0.072276 7.162 0.000
L1.Vorarlberg 0.224330 0.070589 3.178 0.001
L1.Wien -0.230596 0.146974 -1.569 0.117
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.110098 0.174335 0.632 0.528
L1.Burgenland 0.028814 0.087748 0.328 0.743
L1.Kärnten -0.116043 0.070542 -1.645 0.100
L1.Niederösterreich 0.175552 0.206024 0.852 0.394
L1.Oberösterreich 0.018146 0.174559 0.104 0.917
L1.Salzburg 0.225721 0.090404 2.497 0.013
L1.Steiermark 0.144835 0.126592 1.144 0.253
L1.Tirol 0.089860 0.083453 1.077 0.282
L1.Vorarlberg 0.037934 0.081505 0.465 0.642
L1.Wien 0.296195 0.169703 1.745 0.081
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.583328 0.097130 6.006 0.000
L1.Burgenland -0.018627 0.048888 -0.381 0.703
L1.Kärnten 0.000604 0.039302 0.015 0.988
L1.Niederösterreich -0.026538 0.114785 -0.231 0.817
L1.Oberösterreich 0.283999 0.097254 2.920 0.003
L1.Salzburg 0.011389 0.050368 0.226 0.821
L1.Steiermark 0.004112 0.070530 0.058 0.954
L1.Tirol 0.076839 0.046495 1.653 0.098
L1.Vorarlberg 0.180577 0.045410 3.977 0.000
L1.Wien -0.090310 0.094549 -0.955 0.339
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.130280 -0.020795 0.194709 0.237402 0.037599 0.091104 -0.117485 0.151949
Kärnten 0.130280 1.000000 -0.016579 0.181427 0.131625 -0.159119 0.163924 0.018405 0.292115
Niederösterreich -0.020795 -0.016579 1.000000 0.246609 0.071573 0.185977 0.076781 0.017692 0.337150
Oberösterreich 0.194709 0.181427 0.246609 1.000000 0.269147 0.276370 0.091179 0.055543 0.082682
Salzburg 0.237402 0.131625 0.071573 0.269147 1.000000 0.139949 0.056066 0.065768 -0.041867
Steiermark 0.037599 -0.159119 0.185977 0.276370 0.139949 1.000000 0.095111 0.069060 -0.161223
Tirol 0.091104 0.163924 0.076781 0.091179 0.056066 0.095111 1.000000 0.133690 0.121394
Vorarlberg -0.117485 0.018405 0.017692 0.055543 0.065768 0.069060 0.133690 1.000000 0.079050
Wien 0.151949 0.292115 0.337150 0.082682 -0.041867 -0.161223 0.121394 0.079050 1.000000